Search Results for "benchmark" - Page 4
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Showing 243 open source projects for "benchmark"
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gradslam
gradslam is an open source differentiable dense SLAM library
gradslam is an open-source framework providing differentiable building blocks for simultaneous localization and mapping (SLAM) systems. We enable the usage of dense SLAM subsystems from the comfort of PyTorch. The question of “representation” is central in the context of dense simultaneous localization and mapping (SLAM). Newer learning-based approaches have the potential to leverage data or task performance to directly inform the choice of representation. However, learning representations... -
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netstress is a client/server utility designed to stress & benchmark network activity of a given ethernet device or path using simulated (random) real world data and packet sizes instead of fixed data and packet sizes.
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progrep
Utility to show live progress, status & stats for running simulations
progrep is a command-line tool (Linux) to show live progress report, status & stats of a running simulation or compute job that executes a given number of iterations. It shows % completed, time remaining, time elapsed, number of threads, MPI_Rank(if any), CPU usage & speed (FPS). The FPS measures may be used in benchmarking, e.g. while optimizing HPC algorithms for performance. progrep supports both single-threaded and parallel (multicore/multinode - e.g. OpenMP/MPI) jobs. progrep can... -
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Stair Speedtest Reborn
Proxy performance batch tester based on Shadowsocks(R) and V2Ray
Stair Speedtest Reborn is a C++ proxy performance testing application that replaces the original Stair Speedtest script with a faster cross-platform implementation. It can benchmark batches of proxy nodes and report network performance characteristics. Supported ecosystems include Shadowsocks, ShadowsocksR, V2Ray, Trojan, and SOCKS5 configurations. Users can run tests through either a command-line interface or a web interface. Subscription results are saved to logs and can also be rendered as PNG reports for easier comparison. ... -
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Sparse Attention
"Generating Long Sequences with Sparse Transformers" examples
...It explores how modifying the self-attention mechanism with sparse patterns can reduce the quadratic scaling of standard transformers, making it possible to model much longer sequences efficiently. The repository provides implementations of sparse attention layers, training code, and evaluation scripts for benchmark datasets. It highlights both fixed and learnable sparsity patterns that trade off computational cost and model expressiveness. By enabling tractable training on longer contexts, the project opened the door to applications in large-scale text and image generation. Though archived, it remains a key reference for efficient transformer research, influencing many later architectures that aim to extend sequence length while reducing compute. -
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A Windows GUI program that helps to set various parameters of hashcat. The following programs are not included, and should be provided by user. - hashcat (v6.1.1 was used and tested for this project) - CAP, HCCAPX file converter. Free as it is, I have no obligation to update, upgrade or debug it on demand.
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sysbench
Scriptable database and system performance benchmark
...Can be used as a general-purpose Lua interpreter as well, simply replace #!/usr/bin/lua with #!/usr/bin/sysbench in your script. Execute events for this many seconds with statistics disabled before the actual benchmark run with statistics enabled. This is useful when you want to exclude the initial period of a benchmark run from statistics. -
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UNMAINTAINED: Try https://github.com/pjoe/loadem instead This project aims to provide a tool for load testing web applications. The goal is a tool that is easy to use and provides near real-time performance measurements of the application under test which is very usefull during optimization.
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jsprit
Open source toolkit for solving rich vehicle routing problems
...Setting up the problem, defining additional constraints, modifying the algorithms and visualizing the discovered solutions is as easy and handy as reading classical VRP instances to benchmark your algorithm. It is fit for change and extension due to its modular design and a comprehensive set of unit and integration tests. Possibility to define additional stateless and stateful constraints/conditions to account for the richness of your problem. GraphHopper invests in an active open source community. Our flagships are the GraphHopper routing engine and jsprit, the toolkit for solving rich vehicle routing problems. ... -
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Yandex Tank
Load and performance benchmark tool
Yandex.Tank is an extensible open-source load testing tool for advanced Linux users which is especially good as a part of an automated load testing suite. Different load generators are supported. Evgeniy Mamchits' phantom is a very fast (100 000+ RPS) shooter written in C++ (default) JMeter is an extendable and widely known one. BFG is a Python-based generator that allows you to write your load scenarios in Python. Experimental Golang generator: pandora. Performance analytics backend... -
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List
An immutable list with unmatched performance and functional API
An immutable list with unmatched performance and a comprehensive functional API. List is a purely functional alternative to arrays. It is an implementation of a fast persistent sequence data structure. Compared to JavaScript Array List has three major benefits. List is immutable. This makes it safer and better suited for functional programming. It doesn't tempt you with an imperative API and accidental mutations won't be a source of bugs. Since List doesn't allow mutations it can be heavily... -
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I3D models trained on Kinetics
Convolutional neural network model for video classification
...This repository includes pretrained I3D models on the Kinetics dataset, with both RGB and optical flow input streams. The models have achieved state-of-the-art results on benchmark datasets such as UCF101 and HMDB51, and also won first place in the CVPR 2017 Charades Challenge. The project provides TensorFlow and Sonnet-based implementations, pretrained checkpoints, and example scripts for evaluating or fine-tuning models. It also offers sample data, including preprocessed video frames and optical flow arrays, to demonstrate how to run inference and visualize outputs. -
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benchm-ml
A benchmark of commonly used open source implementations
This repository is designed to provide a minimal benchmark framework comparing commonly used machine learning libraries in terms of scalability, speed, and classification accuracy. The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. ... -
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Dopamine
Framework for prototyping of reinforcement learning algorithms
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. It aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research). This first version focuses on supporting the state-of-the-art, single-GPU Rainbow agent (Hessel et al., 2018) applied to Atari 2600 game-playing (Bellemare et al., 2013). Specifically, our Rainbow agent implements the three components identified as most important... -
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RefineNet
RefineNet: Multi-Path Refinement Networks
...It implements the architecture presented in the CVPR 2017 paper RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation and its extended version published in TPAMI 2019. The framework uses multi-path refinement and improved residual pooling to achieve high-quality segmentation results across multiple benchmark datasets. It provides trained models for datasets such as PASCAL VOC 2012, Cityscapes, NYUDv2, Person_Parts, PASCAL_Context, SUNRGBD, and ADE20k, with versions based on ResNet-101 and ResNet-152 backbones. The repository supports both single-scale and multi-scale prediction, with scripts for training, testing, and evaluating segmentation performance. ... -
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Hero
A handy, fast and powerful go template engine
Hero is a handy, fast and powerful go template engine, which pre-compiles the HTML templtes to go code. It has been used in a production environment in bthub.io. The function definition statement defines the function which represents a html file. The function defined should return one and only one parameter []byte. Extend statement states the parent template the current template extends. Include statement includes a sub-template to the current template. It works like #include in C++. Import... -
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HOBBIT Java SDK Example
Integrate your benchmarks and systems into the HOBBIT platform
...You may specify input parameters models for benchmark and system you are running. -
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Omniglot
Omniglot data set for one-shot learning
...The dataset provides both an image representation of each character and the time-ordered stroke coordinates ([x, y, t]) for each instance. Includes stroke data (time-sequenced coordinates) per sample. The repository is intended as a benchmark dataset in few-shot / meta-learning research, not as a plug-and-play detection or classification engine. Pre-split “background” and “evaluation” alphabets for standard benchmarking. -
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Ceras
Universal binary serializer for a wide variety of scenarios
...Ceras generally ranks at the top end of the performance spectrum, together with NetSerializer and MessagePack-CSharp. To get an idea of how Ceras performs here are the preliminary benchmark results. The resulting binary size is about the same as MessagePack-CSharp. Don't forget to tune the settings in SerializerConfig for your specific situation. Using Ceras to read/write network packets might require different settings than, lets say, saving a settings-object to a file, or persisting items/spells/monsters in a game. -
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Video Nonlocal Net
Non-local Neural Networks for Video Classification
...Efficient implementations keep memory and compute manageable so the blocks can be added without rewriting the entire backbone. The result is a practical, drop-in mechanism for upgrading purely local video models into context-aware networks with strong benchmark performance. -
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coNCePTuaL
DSL for writing communication benchmarks
coNCePTuaL is a toolset for rapidly generating portable, readable, and reproducible network-performance tests. coNCePTuaL can perform the equivalent of many pages of C code with just a few mouse clicks or lines of code in a domain-specific language. -
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Open Source web application tester program for load testing web applications.Record ajax requests.Tested on asp.net applications, but works with jsp, php or other.Generates a C# class that you can modify, the class has functions for handling the query/po
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SFD
S³FD: Single Shot Scale-invariant Face Detector, ICCV, 2017
S³FD (Single Shot Scale-invariant Face Detector) is a real-time face detection framework designed to handle faces of various sizes with high accuracy using a single deep neural network. Developed by Shifeng Zhang, S³FD introduces a scale-compensation anchor matching strategy and enhanced detection architecture that makes it especially effective for detecting small faces—a long-standing challenge in face detection research. The project builds upon the SSD framework in Caffe, with... -
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libaco
A blazing fast and lightweight C asymmetric coroutine library
...Along with the implementation of a production-ready C coroutine library, here is detailed documentation about how to implement the fastest and correct coroutine library and also with strict mathematical proof. It has no more than 700 LOC but has the full functionality which you may want from a coroutine library. The benchmark part shows that a context switch between coroutines only takes about 10 ns (in the case of the standalone stack) on the AWS c5d.large machine. User could choose to create a new coroutine with a standalone stack or with a shared stack (could be shared with others). It is extremely memory-efficient, 10,000,000 coroutines simultaneously to run cost only 2.8 GB physical memory (run with tcmalloc, each coroutine has a 120B copy-stack size configuration). ... -
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cocolib / light field suite
CUDA library for continuous optimization and light field analysis
...Implements several recent algorithms for inverse problems and image segmentation with total variation regularizers and vectorial multilabel transition costs. Also included is a suite for variational light field analysis, which ties into the HCI light field benchmark set and givens reference implementations for a number of our recently published algorithms. *** NOTE: documentation on the SourceForge page is outdated and not updated anymore, please visit http://cocolib.net ***
